Inman, Brant A. M.D. wrote:
S-experts:
I am using the Design and Hmisc libraries to fit a logistic regression
model in a manner similar to that given below (note that this model may
not make sense, I just use it to demonstrate my question):
---------------------------
library(Hmisc)
library(Design)
data(pbc)
pbc$ascites[pbc$ascites==-9] <- NA
pbc$ascites <- as.factor(pbc$ascites)
pbc$edema <- as.factor(pbc$edema)
pbc$hepmeg[pbc$hepmeg==-9] <- NA
pbc$hepmeg <- as.factor(pbc$hepmeg)
pbc$stage[pbc$stage==-9] <- NA
pbc$stage <- as.ordered(pbc$stage)
attach(pbc)
The attach had no effect for your code, it just clutters up the search
list and should be avoided. And whenever you have multiple $ there is a
better way. See for example the Hmisc upData function.
ddist <- datadist(pbc)
options(datadist='ddist')
fit <- lrm(edema ~ age + alb + ascites + hepmeg, data=pbc)
--------------------------
Since my goal is to extract the odds ratios from this model, I have
tried two different methods: the obvious method of exponentiation of the
regression coefficients and using the summary.Design function.
--------------------------
exp(fit$coef)
summary(fit)
--------------------------
The problem that I have is with the "summary(fit)" output, namely that
its odds ratios are different from those of "exp(fit$coef)". From the
documentation that is provided by the function's author, I surmise that
the reason the odds ratios for the continuous variables are different is
that the function calculates the odds ratios using "inter-quartile
effects". I have not previously encountered this method of computing
odds ratios and would appreciate any advice from the experts regarding
the reasons for using the inter-quartile method for calculating the odds
ratios rather than the usual method. Which method should be reported in
a paper and is there evidence/publications to support this choice?
Please see my book Regression Modeling Strategies which details the
reasons for this and for not doing exp(coef). A one-unit change is not
meaningful for many of the variables we see in biomedical research.
Frank
Brant Inman
Mayo Clinic
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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